The NExT++ laboratory at the National University of Singapore and Liu Zhiyuan's team at Tsinghua University collaborated to develop a powerful multi-modal large model that integrates a detection and segmentation module, significantly simplifying the matting process. Users only need to use natural language to describe the target object, and the model can quickly and accurately label it and provide corresponding text explanations. This breakthrough technology has demonstrated excellent performance on multiple datasets, especially on referent segmentation and REC tasks.
The large multi-modal model created by the NExT++ laboratory of the National University of Singapore and Liu Zhiyuan's team at Tsinghua University integrates a detection and segmentation module, making image matting easier. By describing requirements in natural language, the model can quickly mark the objects it is looking for and provide text explanations. This model has excellent experimental performance on multiple task data sets and has good ability to refer to segmentation and REC tasks. In addition, this model also introduces a position modeling method based on embedding, which has better position modeling capabilities. Through the optimization of the training process, the model can also achieve good performance on segmentation tasks with scarce annotations.
The model's embedding-based position modeling method and optimized training process enable it to achieve satisfactory results in segmentation tasks with scarce data annotations, demonstrating its strong adaptability and practicability, and laying the foundation for future multi-modal modalities. The development of the model provides new directions and ideas. This research result is expected to have a wide impact in image processing and artificial intelligence related fields.